Overview

Dataset statistics

Number of variables18
Number of observations14546
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 MiB
Average record size in memory202.9 B

Variable types

Numeric15
Categorical3

Alerts

CH has constant value "1"Constant
THR has constant value "40"Constant
A-FRQ is highly overall correlated with ABS-ENERGY and 4 other fieldsHigh correlation
ABS-ENERGY is highly overall correlated with A-FRQ and 7 other fieldsHigh correlation
AMP is highly overall correlated with A-FRQ and 6 other fieldsHigh correlation
ASL is highly overall correlated with RMSHigh correlation
COUN is highly overall correlated with A-FRQ and 7 other fieldsHigh correlation
DURATION is highly overall correlated with ABS-ENERGY and 5 other fieldsHigh correlation
ENER is highly overall correlated with ABS-ENERGY and 5 other fieldsHigh correlation
I-FRQ is highly overall correlated with A-FRQ and 1 other fieldsHigh correlation
LOAD_PAC is highly overall correlated with TIME and 1 other fieldsHigh correlation
PCNTS is highly overall correlated with ABS-ENERGY and 5 other fieldsHigh correlation
R-FRQ is highly overall correlated with A-FRQ and 3 other fieldsHigh correlation
RISE is highly overall correlated with I-FRQ and 1 other fieldsHigh correlation
RMS is highly overall correlated with ASLHigh correlation
SIG STRNGTH is highly overall correlated with ABS-ENERGY and 5 other fieldsHigh correlation
TIME is highly overall correlated with LOAD_PAC and 1 other fieldsHigh correlation
load level is highly overall correlated with LOAD_PAC and 1 other fieldsHigh correlation
RMS is highly skewed (γ1 = 20.60805763)Skewed
ABS-ENERGY is highly skewed (γ1 = 30.21129048)Skewed
TIME has unique valuesUnique
R-FRQ has 171 (1.2%) zerosZeros

Reproduction

Analysis started2025-01-20 01:34:07.616569
Analysis finished2025-01-20 01:34:41.426648
Duration33.81 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

TIME
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct14546
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3296.0188
Minimum182.14624
Maximum5948.7496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:41.563950image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum182.14624
5-th percentile897.05187
Q12301.9195
median2986.1705
Q34224.1322
95-th percentile5633.9746
Maximum5948.7496
Range5766.6034
Interquartile range (IQR)1922.2127

Descriptive statistics

Standard deviation1265.7399
Coefficient of variation (CV)0.38402084
Kurtosis-0.64881325
Mean3296.0188
Median Absolute Deviation (MAD)1047.5658
Skewness0.07047632
Sum47943889
Variance1602097.5
MonotonicityStrictly increasing
2025-01-19T19:34:41.706580image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
182.146238 1
 
< 0.1%
4161.254647 1
 
< 0.1%
4158.694689 1
 
< 0.1%
4158.71421 1
 
< 0.1%
4158.731117 1
 
< 0.1%
4158.741071 1
 
< 0.1%
4158.765663 1
 
< 0.1%
4158.821816 1
 
< 0.1%
4159.042566 1
 
< 0.1%
4159.156316 1
 
< 0.1%
Other values (14536) 14536
99.9%
ValueCountFrequency (%)
182.146238 1
< 0.1%
182.1475745 1
< 0.1%
182.151222 1
< 0.1%
192.3989935 1
< 0.1%
195.3003592 1
< 0.1%
195.308984 1
< 0.1%
198.3451835 1
< 0.1%
202.553552 1
< 0.1%
202.6152367 1
< 0.1%
203.6011248 1
< 0.1%
ValueCountFrequency (%)
5948.749615 1
< 0.1%
5947.666662 1
< 0.1%
5942.457182 1
< 0.1%
5941.394401 1
< 0.1%
5941.220293 1
< 0.1%
5941.025742 1
< 0.1%
5940.243735 1
< 0.1%
5939.310339 1
< 0.1%
5938.169871 1
< 0.1%
5935.872409 1
< 0.1%

LOAD_PAC
Real number (ℝ)

HIGH CORRELATION 

Distinct2673
Distinct (%)18.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2982.164
Minimum-39.97925
Maximum4967.6567
Zeros0
Zeros (%)0.0%
Negative3
Negative (%)< 0.1%
Memory size113.8 KiB
2025-01-19T19:34:41.847363image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum-39.97925
5-th percentile795.151
Q12318.0973
median3151.6548
Q33626.7563
95-th percentile4523.0927
Maximum4967.6567
Range5007.636
Interquartile range (IQR)1308.6591

Descriptive statistics

Standard deviation1128.2578
Coefficient of variation (CV)0.37833524
Kurtosis-0.48555053
Mean2982.164
Median Absolute Deviation (MAD)750.20175
Skewness-0.40660339
Sum43378558
Variance1272965.6
MonotonicityNot monotonic
2025-01-19T19:34:41.991762image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4494.259 68
 
0.5%
3586.78225 62
 
0.4%
3593.07325 56
 
0.4%
4497.4045 56
 
0.4%
3598.31575 54
 
0.4%
3585.2095 54
 
0.4%
2689.26625 52
 
0.4%
3578.9185 51
 
0.4%
2679.82975 50
 
0.3%
3582.064 50
 
0.3%
Other values (2663) 13993
96.2%
ValueCountFrequency (%)
-39.97925 1
 
< 0.1%
-25.8245 1
 
< 0.1%
-19.5335 1
 
< 0.1%
246.26125 2
< 0.1%
257.2705 1
 
< 0.1%
258.84325 1
 
< 0.1%
260.416 1
 
< 0.1%
262.513 3
< 0.1%
264.08575 3
< 0.1%
267.23125 1
 
< 0.1%
ValueCountFrequency (%)
4967.65675 1
 
< 0.1%
4966.084 1
 
< 0.1%
4964.51125 3
< 0.1%
4961.36575 3
< 0.1%
4959.793 1
 
< 0.1%
4958.22025 3
< 0.1%
4955.07475 3
< 0.1%
4953.502 1
 
< 0.1%
4951.405 4
< 0.1%
4948.2595 3
< 0.1%

CH
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size824.0 KiB
1
14546 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters14546
Distinct characters1
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14546
100.0%

Length

2025-01-19T19:34:42.142715image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T19:34:42.260800image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
1 14546
100.0%

Most occurring characters

ValueCountFrequency (%)
1 14546
100.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14546
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 14546
100.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14546
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 14546
100.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14546
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 14546
100.0%

RISE
Real number (ℝ)

HIGH CORRELATION 

Distinct458
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.567372
Minimum1
Maximum1305
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:42.386389image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q130
median65
Q3121
95-th percentile231
Maximum1305
Range1304
Interquartile range (IQR)91

Descriptive statistics

Standard deviation79.694109
Coefficient of variation (CV)0.92060214
Kurtosis16.398806
Mean86.567372
Median Absolute Deviation (MAD)41
Skewness2.6040259
Sum1259209
Variance6351.1509
MonotonicityNot monotonic
2025-01-19T19:34:42.537206image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 228
 
1.6%
3 179
 
1.2%
13 178
 
1.2%
16 167
 
1.1%
23 157
 
1.1%
25 155
 
1.1%
19 138
 
0.9%
28 137
 
0.9%
31 135
 
0.9%
20 132
 
0.9%
Other values (448) 12940
89.0%
ValueCountFrequency (%)
1 57
 
0.4%
2 16
 
0.1%
3 179
1.2%
4 228
1.6%
5 101
0.7%
6 95
0.7%
7 115
0.8%
8 97
0.7%
9 98
0.7%
10 126
0.9%
ValueCountFrequency (%)
1305 1
< 0.1%
1145 1
< 0.1%
1047 1
< 0.1%
957 1
< 0.1%
932 1
< 0.1%
915 1
< 0.1%
900 1
< 0.1%
802 1
< 0.1%
785 1
< 0.1%
735 1
< 0.1%

COUN
Real number (ℝ)

HIGH CORRELATION 

Distinct164
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.468995
Minimum2
Maximum293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:42.676490image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile5
Q111
median19
Q335
95-th percentile74
Maximum293
Range291
Interquartile range (IQR)24

Descriptive statistics

Standard deviation22.978326
Coefficient of variation (CV)0.86812235
Kurtosis6.7267331
Mean26.468995
Median Absolute Deviation (MAD)10
Skewness2.0897964
Sum385018
Variance528.00347
MonotonicityNot monotonic
2025-01-19T19:34:42.821120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 604
 
4.2%
11 600
 
4.1%
9 598
 
4.1%
12 551
 
3.8%
8 543
 
3.7%
13 471
 
3.2%
7 463
 
3.2%
14 463
 
3.2%
6 452
 
3.1%
15 427
 
2.9%
Other values (154) 9374
64.4%
ValueCountFrequency (%)
2 42
 
0.3%
3 172
 
1.2%
4 260
1.8%
5 358
2.5%
6 452
3.1%
7 463
3.2%
8 543
3.7%
9 598
4.1%
10 604
4.2%
11 600
4.1%
ValueCountFrequency (%)
293 1
< 0.1%
224 1
< 0.1%
203 1
< 0.1%
196 1
< 0.1%
194 1
< 0.1%
192 1
< 0.1%
191 1
< 0.1%
177 1
< 0.1%
171 1
< 0.1%
169 1
< 0.1%

ENER
Real number (ℝ)

HIGH CORRELATION 

Distinct111
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3243503
Minimum1
Maximum590
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:42.961032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile18
Maximum590
Range589
Interquartile range (IQR)4

Descriptive statistics

Standard deviation12.057225
Coefficient of variation (CV)2.2645439
Kurtosis575.86286
Mean5.3243503
Median Absolute Deviation (MAD)1
Skewness17.330385
Sum77448
Variance145.37667
MonotonicityNot monotonic
2025-01-19T19:34:43.103723image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 4676
32.1%
2 2782
19.1%
3 1729
 
11.9%
4 1131
 
7.8%
5 810
 
5.6%
6 552
 
3.8%
7 480
 
3.3%
8 327
 
2.2%
9 252
 
1.7%
10 232
 
1.6%
Other values (101) 1575
 
10.8%
ValueCountFrequency (%)
1 4676
32.1%
2 2782
19.1%
3 1729
 
11.9%
4 1131
 
7.8%
5 810
 
5.6%
6 552
 
3.8%
7 480
 
3.3%
8 327
 
2.2%
9 252
 
1.7%
10 232
 
1.6%
ValueCountFrequency (%)
590 1
< 0.1%
388 1
< 0.1%
375 1
< 0.1%
236 1
< 0.1%
229 1
< 0.1%
222 1
< 0.1%
217 1
< 0.1%
213 1
< 0.1%
205 1
< 0.1%
190 1
< 0.1%

DURATION
Real number (ℝ)

HIGH CORRELATION 

Distinct1189
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean363.75959
Minimum46
Maximum3163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:43.243220image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile118
Q1196
median295
Q3460
95-th percentile822
Maximum3163
Range3117
Interquartile range (IQR)264

Descriptive statistics

Standard deviation245.9997
Coefficient of variation (CV)0.67627
Kurtosis10.074783
Mean363.75959
Median Absolute Deviation (MAD)117
Skewness2.3064125
Sum5291247
Variance60515.852
MonotonicityNot monotonic
2025-01-19T19:34:43.378582image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202 62
 
0.4%
198 58
 
0.4%
248 56
 
0.4%
195 55
 
0.4%
190 52
 
0.4%
200 52
 
0.4%
222 52
 
0.4%
196 50
 
0.3%
228 49
 
0.3%
199 49
 
0.3%
Other values (1179) 14011
96.3%
ValueCountFrequency (%)
46 1
 
< 0.1%
48 1
 
< 0.1%
51 1
 
< 0.1%
52 1
 
< 0.1%
53 1
 
< 0.1%
55 2
 
< 0.1%
56 1
 
< 0.1%
57 3
< 0.1%
58 1
 
< 0.1%
59 5
< 0.1%
ValueCountFrequency (%)
3163 1
< 0.1%
2970 1
< 0.1%
2834 1
< 0.1%
2732 1
< 0.1%
2628 1
< 0.1%
2483 1
< 0.1%
2437 1
< 0.1%
2379 1
< 0.1%
2303 1
< 0.1%
2237 1
< 0.1%

AMP
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49.430428
Minimum40
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:43.518800image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile42
Q145
median48
Q352
95-th percentile61
Maximum90
Range50
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.9530309
Coefficient of variation (CV)0.12043252
Kurtosis2.6734666
Mean49.430428
Median Absolute Deviation (MAD)3
Skewness1.3717691
Sum719015
Variance35.438576
MonotonicityNot monotonic
2025-01-19T19:34:43.664270image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
44 1317
 
9.1%
46 1315
 
9.0%
47 1299
 
8.9%
45 1246
 
8.6%
48 1061
 
7.3%
49 955
 
6.6%
50 900
 
6.2%
43 790
 
5.4%
51 737
 
5.1%
52 610
 
4.2%
Other values (38) 4316
29.7%
ValueCountFrequency (%)
40 19
 
0.1%
41 201
 
1.4%
42 539
3.7%
43 790
5.4%
44 1317
9.1%
45 1246
8.6%
46 1315
9.0%
47 1299
8.9%
48 1061
7.3%
49 955
6.6%
ValueCountFrequency (%)
90 2
< 0.1%
88 1
 
< 0.1%
85 1
 
< 0.1%
84 2
< 0.1%
83 2
< 0.1%
82 1
 
< 0.1%
81 2
< 0.1%
80 4
< 0.1%
79 1
 
< 0.1%
78 2
< 0.1%

A-FRQ
Real number (ℝ)

HIGH CORRELATION 

Distinct173
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean72.125877
Minimum6
Maximum182
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:43.800057image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile22
Q147
median71
Q395
95-th percentile129
Maximum182
Range176
Interquartile range (IQR)48

Descriptive statistics

Standard deviation32.783608
Coefficient of variation (CV)0.45453324
Kurtosis-0.43442171
Mean72.125877
Median Absolute Deviation (MAD)24
Skewness0.30707494
Sum1049143
Variance1074.765
MonotonicityNot monotonic
2025-01-19T19:34:44.035770image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
63 190
 
1.3%
73 173
 
1.2%
68 172
 
1.2%
67 170
 
1.2%
85 169
 
1.2%
79 168
 
1.2%
78 166
 
1.1%
80 165
 
1.1%
52 165
 
1.1%
70 165
 
1.1%
Other values (163) 12843
88.3%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 8
 
0.1%
8 8
 
0.1%
9 18
 
0.1%
10 25
0.2%
11 22
 
0.2%
12 39
0.3%
13 34
0.2%
14 57
0.4%
15 62
0.4%
ValueCountFrequency (%)
182 2
< 0.1%
180 2
< 0.1%
176 2
< 0.1%
175 2
< 0.1%
174 1
< 0.1%
173 1
< 0.1%
172 1
< 0.1%
171 2
< 0.1%
170 1
< 0.1%
169 2
< 0.1%

RMS
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct110
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0012573353
Minimum0.0002
Maximum0.1372
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:44.171727image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0.0002
5-th percentile0.0002
Q10.0002
median0.0006
Q30.0012
95-th percentile0.0042
Maximum0.1372
Range0.137
Interquartile range (IQR)0.001

Descriptive statistics

Standard deviation0.0033858844
Coefficient of variation (CV)2.6929048
Kurtosis669.50959
Mean0.0012573353
Median Absolute Deviation (MAD)0.0004
Skewness20.608058
Sum18.2892
Variance1.1464213 × 10-5
MonotonicityNot monotonic
2025-01-19T19:34:44.319489image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0002 5530
38.0%
0.0004 1662
 
11.4%
0.0006 1277
 
8.8%
0.0008 1014
 
7.0%
0.001 809
 
5.6%
0.0012 664
 
4.6%
0.0014 536
 
3.7%
0.0016 426
 
2.9%
0.0018 349
 
2.4%
0.002 290
 
2.0%
Other values (100) 1989
 
13.7%
ValueCountFrequency (%)
0.0002 5530
38.0%
0.0004 1662
 
11.4%
0.0006 1277
 
8.8%
0.0008 1014
 
7.0%
0.001 809
 
5.6%
0.0012 664
 
4.6%
0.0014 536
 
3.7%
0.0016 426
 
2.9%
0.0018 349
 
2.4%
0.002 290
 
2.0%
ValueCountFrequency (%)
0.1372 3
< 0.1%
0.084 3
< 0.1%
0.0806 1
 
< 0.1%
0.0786 1
 
< 0.1%
0.051 1
 
< 0.1%
0.0488 1
 
< 0.1%
0.0456 2
< 0.1%
0.0426 1
 
< 0.1%
0.0388 2
< 0.1%
0.0372 1
 
< 0.1%

ASL
Real number (ℝ)

HIGH CORRELATION 

Distinct51
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.575141
Minimum9
Maximum65
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:44.461414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile10
Q112
median18
Q325
95-th percentile35
Maximum65
Range56
Interquartile range (IQR)13

Descriptive statistics

Standard deviation8.5489888
Coefficient of variation (CV)0.43672681
Kurtosis0.37312649
Mean19.575141
Median Absolute Deviation (MAD)7
Skewness0.84279707
Sum284740
Variance73.085209
MonotonicityNot monotonic
2025-01-19T19:34:44.592728image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 2147
 
14.8%
11 1246
 
8.6%
12 853
 
5.9%
13 658
 
4.5%
14 574
 
3.9%
22 558
 
3.8%
23 552
 
3.8%
16 518
 
3.6%
19 511
 
3.5%
21 499
 
3.4%
Other values (41) 6430
44.2%
ValueCountFrequency (%)
9 25
 
0.2%
10 2147
14.8%
11 1246
8.6%
12 853
 
5.9%
13 658
 
4.5%
14 574
 
3.9%
15 467
 
3.2%
16 518
 
3.6%
17 466
 
3.2%
18 462
 
3.2%
ValueCountFrequency (%)
65 3
 
< 0.1%
61 4
 
< 0.1%
60 1
 
< 0.1%
57 1
 
< 0.1%
56 3
 
< 0.1%
55 1
 
< 0.1%
54 7
< 0.1%
52 1
 
< 0.1%
51 8
0.1%
50 10
0.1%

PCNTS
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7841331
Minimum1
Maximum112
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:44.727092image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median6
Q310
95-th percentile20.75
Maximum112
Range111
Interquartile range (IQR)7

Descriptive statistics

Standard deviation6.5796345
Coefficient of variation (CV)0.84526233
Kurtosis15.778603
Mean7.7841331
Median Absolute Deviation (MAD)3
Skewness2.6262149
Sum113228
Variance43.29159
MonotonicityNot monotonic
2025-01-19T19:34:44.862442image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1612
11.1%
3 1599
11.0%
4 1543
10.6%
5 1422
9.8%
6 1118
 
7.7%
7 983
 
6.8%
8 812
 
5.6%
9 718
 
4.9%
1 683
 
4.7%
10 591
 
4.1%
Other values (51) 3465
23.8%
ValueCountFrequency (%)
1 683
4.7%
2 1612
11.1%
3 1599
11.0%
4 1543
10.6%
5 1422
9.8%
6 1118
7.7%
7 983
6.8%
8 812
5.6%
9 718
4.9%
10 591
 
4.1%
ValueCountFrequency (%)
112 1
 
< 0.1%
93 1
 
< 0.1%
90 1
 
< 0.1%
89 1
 
< 0.1%
72 1
 
< 0.1%
71 1
 
< 0.1%
58 1
 
< 0.1%
57 2
< 0.1%
55 3
< 0.1%
53 2
< 0.1%

THR
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size838.2 KiB
40
14546 

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters29092
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row40
2nd row40
3rd row40
4th row40
5th row40

Common Values

ValueCountFrequency (%)
40 14546
100.0%

Length

2025-01-19T19:34:44.998800image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-19T19:34:45.098524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
ValueCountFrequency (%)
40 14546
100.0%

Most occurring characters

ValueCountFrequency (%)
4 14546
50.0%
0 14546
50.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 29092
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4 14546
50.0%
0 14546
50.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 29092
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4 14546
50.0%
0 14546
50.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 29092
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4 14546
50.0%
0 14546
50.0%

R-FRQ
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct178
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.165063
Minimum0
Maximum333
Zeros171
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:45.208816image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q138
median62
Q388
95-th percentile128
Maximum333
Range333
Interquartile range (IQR)50

Descriptive statistics

Standard deviation37.200562
Coefficient of variation (CV)0.57086666
Kurtosis4.525512
Mean65.165063
Median Absolute Deviation (MAD)25
Skewness1.1383566
Sum947891
Variance1383.8818
MonotonicityNot monotonic
2025-01-19T19:34:45.347937image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 171
 
1.2%
42 171
 
1.2%
45 167
 
1.1%
54 162
 
1.1%
52 162
 
1.1%
65 161
 
1.1%
81 161
 
1.1%
70 160
 
1.1%
40 160
 
1.1%
48 159
 
1.1%
Other values (168) 12912
88.8%
ValueCountFrequency (%)
0 171
1.2%
3 3
 
< 0.1%
4 14
 
0.1%
5 28
 
0.2%
6 48
 
0.3%
7 50
 
0.3%
8 60
 
0.4%
9 58
 
0.4%
10 78
0.5%
11 85
0.6%
ValueCountFrequency (%)
333 22
0.2%
250 29
0.2%
222 6
 
< 0.1%
200 12
0.1%
187 3
 
< 0.1%
181 1
 
< 0.1%
178 1
 
< 0.1%
176 2
 
< 0.1%
173 7
 
< 0.1%
172 1
 
< 0.1%

I-FRQ
Real number (ℝ)

HIGH CORRELATION 

Distinct240
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.84772
Minimum5
Maximum1000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:45.519176image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile29
Q174
median115
Q3157
95-th percentile250
Maximum1000
Range995
Interquartile range (IQR)83

Descriptive statistics

Standard deviation86.688056
Coefficient of variation (CV)0.69435031
Kurtosis39.742096
Mean124.84772
Median Absolute Deviation (MAD)41
Skewness4.499439
Sum1816035
Variance7514.8191
MonotonicityNot monotonic
2025-01-19T19:34:45.691103image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 385
 
2.6%
250 356
 
2.4%
166 343
 
2.4%
333 259
 
1.8%
125 249
 
1.7%
142 247
 
1.7%
153 200
 
1.4%
111 181
 
1.2%
181 170
 
1.2%
100 169
 
1.2%
Other values (230) 11987
82.4%
ValueCountFrequency (%)
5 4
 
< 0.1%
6 4
 
< 0.1%
7 6
 
< 0.1%
8 6
 
< 0.1%
9 16
0.1%
10 21
0.1%
11 19
0.1%
12 20
0.1%
13 23
0.2%
14 24
0.2%
ValueCountFrequency (%)
1000 57
 
0.4%
666 1
 
< 0.1%
500 24
 
0.2%
428 2
 
< 0.1%
400 25
 
0.2%
375 1
 
< 0.1%
363 1
 
< 0.1%
333 259
1.8%
307 4
 
< 0.1%
300 8
 
0.1%

SIG STRNGTH
Real number (ℝ)

HIGH CORRELATION 

Distinct9236
Distinct (%)63.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36152.927
Minimum6246
Maximum3686000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:45.863805image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum6246
5-th percentile7012
Q110767.75
median18209
Q335657.75
95-th percentile116019.75
Maximum3686000
Range3679754
Interquartile range (IQR)24890

Descriptive statistics

Standard deviation75346.797
Coefficient of variation (CV)2.0841134
Kurtosis573.49701
Mean36152.927
Median Absolute Deviation (MAD)9159.5
Skewness17.283107
Sum5.2588047 × 108
Variance5.6771399 × 109
MonotonicityNot monotonic
2025-01-19T19:34:46.034241image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6896 10
 
0.1%
6673 8
 
0.1%
6795 8
 
0.1%
9781 7
 
< 0.1%
8427 7
 
< 0.1%
7555 7
 
< 0.1%
7256 7
 
< 0.1%
8415 7
 
< 0.1%
9495 7
 
< 0.1%
9202 7
 
< 0.1%
Other values (9226) 14471
99.5%
ValueCountFrequency (%)
6246 3
< 0.1%
6249 6
< 0.1%
6253 5
< 0.1%
6256 1
 
< 0.1%
6259 5
< 0.1%
6262 1
 
< 0.1%
6265 1
 
< 0.1%
6268 1
 
< 0.1%
6271 4
< 0.1%
6274 4
< 0.1%
ValueCountFrequency (%)
3686000 1
< 0.1%
2424000 1
< 0.1%
2345000 1
< 0.1%
1477000 1
< 0.1%
1432000 1
< 0.1%
1388000 1
< 0.1%
1358000 1
< 0.1%
1333000 1
< 0.1%
1281000 1
< 0.1%
1193000 1
< 0.1%

ABS-ENERGY
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct8458
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15126.808
Minimum526.477
Maximum3933000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size113.8 KiB
2025-01-19T19:34:46.189201image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Quantile statistics

Minimum526.477
5-th percentile1135
Q11818
median3380
Q39002.25
95-th percentile63405.5
Maximum3933000
Range3932473.5
Interquartile range (IQR)7184.25

Descriptive statistics

Standard deviation55298.653
Coefficient of variation (CV)3.6556723
Kurtosis1835.2268
Mean15126.808
Median Absolute Deviation (MAD)1960
Skewness30.21129
Sum2.2003455 × 108
Variance3.057941 × 109
MonotonicityNot monotonic
2025-01-19T19:34:46.349265image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1449 12
 
0.1%
1915 12
 
0.1%
1358 10
 
0.1%
1272 10
 
0.1%
1351 10
 
0.1%
1301 10
 
0.1%
1494 10
 
0.1%
1252 10
 
0.1%
1616 10
 
0.1%
1886 10
 
0.1%
Other values (8448) 14442
99.3%
ValueCountFrequency (%)
526.477 1
< 0.1%
677.997 1
< 0.1%
716.096 1
< 0.1%
718.842 1
< 0.1%
718.923 1
< 0.1%
721.504 1
< 0.1%
723.072 1
< 0.1%
733.61 1
< 0.1%
733.76 1
< 0.1%
738.329 1
< 0.1%
ValueCountFrequency (%)
3933000 1
< 0.1%
1658000 1
< 0.1%
1304000 1
< 0.1%
1241000 1
< 0.1%
768549 1
< 0.1%
664832 1
< 0.1%
589839 1
< 0.1%
585230 1
< 0.1%
446879 1
< 0.1%
399191 1
< 0.1%

load level
Categorical

HIGH CORRELATION 

Distinct30
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size950.4 KiB
LL4A_RISE
2927 
LL5A_RISE
1661 
LL3A_RISE
1575 
LL3A_CONSTANT
879 
LL4B_RISE
725 
Other values (25)
6779 

Length

Max length15
Median length9
Mean length9.8970164
Min length8

Characters and Unicode

Total characters143962
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowWITHOUT_CLASIFY
2nd rowWITHOUT_CLASIFY
3rd rowWITHOUT_CLASIFY
4th rowLL1_RISE
5th rowLL1_RISE

Common Values

ValueCountFrequency (%)
LL4A_RISE 2927
20.1%
LL5A_RISE 1661
11.4%
LL3A_RISE 1575
10.8%
LL3A_CONSTANT 879
 
6.0%
LL4B_RISE 725
 
5.0%
LL6A_RISE 723
 
5.0%
LL2A_RISE 657
 
4.5%
LL5B_RISE 614
 
4.2%
LL5A_LOW 592
 
4.1%
LL5A_CONSTANT 575
 
4.0%
Other values (20) 3618
24.9%

Length

2025-01-19T19:34:46.523753image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ll4a_rise 2927
20.1%
ll5a_rise 1661
11.4%
ll3a_rise 1575
10.8%
ll3a_constant 879
 
6.0%
ll4b_rise 725
 
5.0%
ll6a_rise 723
 
5.0%
ll2a_rise 657
 
4.5%
ll5b_rise 614
 
4.2%
ll5a_low 592
 
4.1%
ll5a_constant 575
 
4.0%
Other values (20) 3618
24.9%

Most occurring characters

ValueCountFrequency (%)
L 30384
21.1%
_ 14546
10.1%
A 14259
9.9%
S 12542
8.7%
I 10061
 
7.0%
R 9291
 
6.5%
E 9291
 
6.5%
T 7156
 
5.0%
N 6386
 
4.4%
O 5255
 
3.7%
Other values (13) 24791
17.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 143962
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 30384
21.1%
_ 14546
10.1%
A 14259
9.9%
S 12542
8.7%
I 10061
 
7.0%
R 9291
 
6.5%
E 9291
 
6.5%
T 7156
 
5.0%
N 6386
 
4.4%
O 5255
 
3.7%
Other values (13) 24791
17.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 143962
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 30384
21.1%
_ 14546
10.1%
A 14259
9.9%
S 12542
8.7%
I 10061
 
7.0%
R 9291
 
6.5%
E 9291
 
6.5%
T 7156
 
5.0%
N 6386
 
4.4%
O 5255
 
3.7%
Other values (13) 24791
17.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 143962
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 30384
21.1%
_ 14546
10.1%
A 14259
9.9%
S 12542
8.7%
I 10061
 
7.0%
R 9291
 
6.5%
E 9291
 
6.5%
T 7156
 
5.0%
N 6386
 
4.4%
O 5255
 
3.7%
Other values (13) 24791
17.2%

Interactions

2025-01-19T19:34:38.359276image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:08.417144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:10.786524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:12.934971image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:15.032352image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.166002image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:19.204864image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:21.314223image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:23.402164image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:25.487938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:27.630069image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.728048image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.730139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:33.747497image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:35.978063image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:38.530853image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:08.617815image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:10.925662image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:13.068336image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:15.167393image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.300384image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:19.336617image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:21.446734image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:23.540446image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:25.628107image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:27.761033image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.858649image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.863265image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:33.876982image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:36.122359image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:38.678404image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:08.760956image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:11.058539image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:13.200120image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:15.298804image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.431445image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:19.469686image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:21.576162image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:23.676251image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:25.768622image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.012144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.986473image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.996145image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:34.003464image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:36.263113image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:38.828471image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:08.905828image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:11.199845image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:13.330769image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:15.432822image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.565577image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:19.606508image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:21.708814image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:23.815524image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:25.910623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.144363image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:30.118700image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:32.128066image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:34.132150image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:36.410482image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:38.977987image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:09.049551image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:11.343974image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:13.465431image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:15.560686image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.695643image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:19.743632image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:21.841069image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:23.950946image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:26.052977image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.276571image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:30.249581image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:32.257593image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:34.262305image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:36.555412image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:39.127313image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:09.329658image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:11.485327image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:13.598171image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:15.791147image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.828086image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:19.875938image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.063262image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:24.090460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:26.194687image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.407512image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:30.400029image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:32.389388image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:34.510937image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:36.708398image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:39.273455image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:09.471642image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:11.621347image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:13.771529image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:15.925219image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.961730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:20.007095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.196138image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:24.226725image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:26.335685image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.536958image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:30.533350image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:32.523863image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:34.642177image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:36.856024image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:39.424157image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:09.617921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:11.761464image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:13.910474image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:16.059114image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:18.097372image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:20.143017image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.322686image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:24.361461image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:26.477175image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.664884image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:30.663340image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:32.656328image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:34.782376image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:37.007616image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:39.583844image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:09.769496image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:11.908098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:14.054498image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:16.199711image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:18.239067image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:20.290678image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.461089image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:24.500031image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:26.623751image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.802682image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:30.800540image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:32.796427image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:34.922532image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:37.164090image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:39.739577image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:09.918333image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:12.051770image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:14.193730image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:16.338460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:18.379909image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:20.432736image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.596139image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:24.643609image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:26.762747image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:28.934770image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:30.935166image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:32.935079image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:35.065135image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:37.319107image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:39.881124image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:10.054182image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:12.208702image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:14.324109image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:16.466728image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:18.507199image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:20.571408image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.719683image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:24.774945image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:26.898434image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.054459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.055765image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:33.063179image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:35.217292image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:37.459921image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:40.035151image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:10.193085image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:12.354785image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:14.455098image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:16.603930image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:18.638032image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:20.705451image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.846454image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:24.909078image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:27.038459image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.179770image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.179011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:33.191015image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:35.407006image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:37.606588image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:40.184211image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:10.331684image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:12.501476image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:14.586774image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:16.732727image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:18.766824image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:20.840095image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:22.975824image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:25.041757image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:27.177632image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.308354image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.306623image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:33.318414image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:35.550184image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:37.751721image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:40.335241image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:10.476667image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:12.638695image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:14.723900image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:16.868959image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:18.903249image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:21.011783image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:23.108724image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:25.182455image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:27.322530image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.440235image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.440144image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:33.453077image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:35.681911image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:37.976837image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:40.549134image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:10.636718image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:12.788150image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:14.884923image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:17.019964image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:19.053564image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:21.167655image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:23.256225image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:25.336260image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:27.479273image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:29.586383image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:31.585272image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:33.604184image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:35.830579image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
2025-01-19T19:34:38.143011image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/

Correlations

2025-01-19T19:34:46.705255image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A-FRQABS-ENERGYAMPASLCOUNDURATIONENERI-FRQLOAD_PACPCNTSR-FRQRISERMSSIG STRNGTHTIMEload level
A-FRQ1.0000.5900.7110.0650.623-0.0010.4050.6150.0830.3100.911-0.1860.0660.4010.0480.070
ABS-ENERGY0.5901.0000.9110.1010.9610.7500.9470.3000.0480.5900.5410.2150.1010.963-0.0130.000
AMP0.7110.9111.0000.1070.8590.5180.7990.4770.0560.4490.6420.0050.1080.803-0.0030.028
ASL0.0650.1010.1071.0000.1030.0830.0950.0470.0200.0600.0720.0190.9730.097-0.3580.224
COUN0.6230.9610.8590.1031.0000.7520.9310.2900.0590.6500.5720.2490.1020.942-0.0070.029
DURATION-0.0010.7500.5180.0830.7521.0000.873-0.1250.0010.5610.0060.4790.0820.889-0.0590.037
ENER0.4050.9470.7990.0950.9310.8731.0000.1590.0380.6050.3760.3210.0950.978-0.0230.000
I-FRQ0.6150.3000.4770.0470.290-0.1250.1591.0000.035-0.1290.476-0.6650.0490.1510.0130.063
LOAD_PAC0.0830.0480.0560.0200.0590.0010.0380.0351.0000.0540.0760.0070.0070.0360.5750.653
PCNTS0.3100.5900.4490.0600.6500.5610.605-0.1290.0541.0000.2160.7630.0550.6150.0070.000
R-FRQ0.9110.5410.6420.0720.5720.0060.3760.4760.0760.2161.000-0.1470.0740.3700.0360.061
RISE-0.1860.2150.0050.0190.2490.4790.321-0.6650.0070.763-0.1471.0000.0150.330-0.0190.047
RMS0.0660.1010.1080.9730.1020.0820.0950.0490.0070.0550.0740.0151.0000.097-0.3610.000
SIG STRNGTH0.4010.9630.8030.0970.9420.8890.9780.1510.0360.6150.3700.3300.0971.000-0.0270.000
TIME0.048-0.013-0.003-0.358-0.007-0.059-0.0230.0130.5750.0070.036-0.019-0.361-0.0271.0000.915
load level0.0700.0000.0280.2240.0290.0370.0000.0630.6530.0000.0610.0470.0000.0000.9151.000

Missing values

2025-01-19T19:34:41.002444image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-19T19:34:41.303460image/svg+xmlMatplotlib v3.9.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

TIMELOAD_PACCHRISECOUNENERDURATIONAMPA-FRQRMSASLPCNTSTHRR-FRQI-FRQSIG STRNGTHABS-ENERGYload level
0182.146238-25.82450122026446746560.00021017403677257514113.0WITHOUT_CLASIFY
1182.147574-19.53350193219442297057650.0052375540685926562183236.0WITHOUT_CLASIFY
2182.151222-39.9792511302931232834681030.0052371640102123774221399191.0WITHOUT_CLASIFY
3192.398993246.261251921227047780.00021024072222179893404.0LL1_RISE
4195.300359324.8987511919235745250.0002106401831151772040.0LL1_RISE
5195.308984334.3352512020338748520.00082224049100245534618.0LL1_RISE
6198.345183425.55475111321345947460.0002106404353243604102.0LL1_RISE
7202.553552530.92900110432549350650.0014267406467342917002.0LL1_RISE
8202.615237532.501751514126742150.00021024093991901155.0LL1_RISE
9203.601125548.7535019111234947320.0002106401965155312555.0LL1_RISE
TIMELOAD_PACCHRISECOUNENERDURATIONAMPA-FRQRMSASLPCNTSTHRR-FRQI-FRQSIG STRNGTHABS-ENERGYload level
145365935.8724094943.5412515919225649740.00021184055135183674180.0WITHOUT_CLASIFY
145375938.1698714914.7075016276362841100.00021164009215422258.0WITHOUT_CLASIFY
145385939.3103394932.53200126434543549780.00021024405890313366376.0WITHOUT_CLASIFY
145395940.2437354921.5227517329330451950.00021284090109237785166.0WITHOUT_CLASIFY
145405941.0257424935.6775018432563848500.0002117404583338465467.0WITHOUT_CLASIFY
145415941.2202934941.96850141421568946300.00021012403228366915597.0WITHOUT_CLASIFY
145425941.3944014938.8230017617224847690.0002107405892150822869.0WITHOUT_CLASIFY
145435942.4571824929.38650124044989848490.00021020403683563409878.0WITHOUT_CLASIFY
145445947.6666624919.42575113710122542440.0002106404543109591702.0WITHOUT_CLASIFY
145455948.7496154953.50200189174491220.00021024010625067161729.0WITHOUT_CLASIFY